Please write 3 line haiku about a magical evening in London, write using markdown, starting with a H1 heading (formatted like this '# line 1 here...') then H2 (formatted like this '## line 2 here...') then bold then italics then normal text. 3 lines only please.

# A Magical Evening in London
## Lights twinkling bright
**Whispers of enchantment**
*Memories to cherish*

Write a simple, 3 layer CNN in pytorch. then highlight core concepts to know in bold. use markdown formatting for code blocks and clear understanding

Here is a simple 3 layer CNN in PyTorch:

```python
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc = nn.Linear(in_features=64*3*3, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.relu3(x)
        x = self.pool3(x)
        x = x.view(-1, 64*3*3)
        x = self.fc(x)
        return x
```

The core concepts to know in this code are:

- **nn.Module**: This is the base class for all neural network modules in PyTorch. It provides several methods and attributes, including `__init__()` and `forward()`, which we use to define our CNN.
- **nn.Conv2d**: This is a convolutional layer in PyTorch. It takes in a 4D tensor as input (batch_size, channels, height, width) and outputs a 4D tensor of the same shape. The arguments we pass to `nn.Conv2d()` are:
    - `in_channels`: The number of input channels (i.e., the number of input feature maps).
    - `out_channels`: The number of output channels (i.e., the number of filters to learn).
    - `kernel_size`: The size of the convolutional kernel (i.e., the height and width of the filter).
    - `stride`: The stride of the convolution (i.e., the number of pixels to move the filter at each step).
    - `padding`: The number of pixels to add around the input tensor to preserve its spatial dimensions.
- **nn.ReLU**: This is the rectified linear activation function in PyTorch. It applies the element-wise function `max(0, x)` to the input tensor.
- **nn.MaxPool2d**: This is the max pooling layer in PyTorch. It takes in a 4D tensor as input and outputs a 4D tensor of the same shape, but with the height and width dimensions reduced by a factor of `kernel_size/stride`. The arguments we pass to `nn.MaxPool2d()` are:
    - `kernel_size`: The size of the max pooling kernel (i.e., the height and width of the pooling window).
    - `stride`: The stride of the max pooling (i.e., the number of pixels to move the pooling window at each step).
- **nn.Linear**: This is a fully connected layer in PyTorch. It takes in a 2D tensor as input (batch_size, num_features) and outputs a 2D tensor of shape (batch_size, out_features). The arguments we pass to `nn.Linear()` are:
    - `in_features`: The number of input features (i.e., the size of the input tensor along the second dimension).
    - `out_features`: The number of output features (i.e., the size of the output tensor along the second dimension).
- **x.view()**: This is a method of PyTorch tensors that reshapes the tensor while preserving its total number of elements. In the code above, we use it to reshape the output of the last convolutional layer into a 2D tensor that can be fed into the fully connected layer.

Write a 15 line readme with nice markdown formatting for a library that helps with medical imaging

# Medical Imaging Library

The Medical Imaging Library is a collection of tools designed to assist in the processing and analysis of medical images. This library can be used to perform tasks such as image segmentation, registration, and visualization.

## Features

- Image segmentation
- Image registration
- Image visualization
- Support for various medical image formats
- Easy-to-use API

## Installation

To install the Medical Imaging Library, simply run the following command:

```
pip install medical-imaging
```

## Usage

To use the Medical Imaging Library, simply import it in your Python code:

```
import medical_imaging
```

From there, you can use the library's functions to perform various tasks on your medical images.

## Examples

Here are a few examples of how you can use the Medical Imaging Library:

```python
import medical_imaging

# Load an image
image = medical_imaging.load_image('path/to/image.nii')

# Segment the image
segmented_image = medical_imaging.segment_image(image)

# Register two images
registered_image = medical_imaging.register_images(image1, image2)

# Visualize an image
medical_imaging.visualize_image(image)
```

## Contributing

If you would like to contribute to the Medical Imaging Library, please see the [contribution guidelines](CONTRIBUTING.md).

## License

The Medical Imaging Library is licensed under the [MIT License](LICENSE).

## Support

If you encounter any issues while using the Medical Imaging Library, please [submit an issue](https://github.com/username/repo/issues/new) on GitHub.

## Credits

The Medical Imaging Library was created by [Your Name](https://github.com/username). Special thanks to [Contributor Name](https://github.com/contributor) for their contributions.

## Acknowledgements

This project was made possible thanks to the following open source libraries:

- Numpy
- Scipy
- SimpleITK

## Contact

If you have any questions or comments about the Medical Imaging Library, please feel free to [contact me](mailto:youremail@example.com).

Please write 3 line haiku about a magical evening in London, write using markdown, starting with a H1 heading then H2 then bold then italics then normal text. 3 lines only please.

#Magical Evening in London

##Enchanting Lights

**Bright lights glisten,**

*Magical evening in London,*

*Enchanting and divine.*

Write a simple, 3 layer CNN in pytorch. then highlight core concepts to know in bold. use markdown formatting for code blocks and clear understanding

Here is a simple 3 layer CNN in PyTorch:

```python
import torch.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(32 * 8 * 8, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = self.pool(x)
        x = x.view(-1, 32 * 8 * 8)
        x = self.fc(x)
        return x
```

**Core concepts to know:**

1. `nn.Module`: This is a base class for all neural network modules in PyTorch. When creating a new neural network, you should always subclass `nn.Module` and define your layers in the `__init__` method and the forward pass in the `forward` method.

2. Convolutional Layers: Convolutional layers are the core building blocks of a CNN. They apply a set of filters to the input image, which results in a set of feature maps. In this example, we define two convolutional layers: `self.conv1` and `self.conv2`. 

3. Pooling Layers: Pooling layers are used to reduce the spatial dimensions of the feature maps. In this example, we use max pooling, which takes the maximum value in each window of size `kernel_size` and stride `stride`.

4. Activation Functions: Activation functions are used to introduce non-linearity into the network. In this example, we use the ReLU activation function, which sets all negative values to zero.

5. Fully Connected Layers: Fully connected layers are used to map the output of the convolutional layers to a desired output size. In this example, we use a single fully connected layer with 10 output units, which corresponds to the 10 classes in the CIFAR-10 dataset.

6. `x.view()`: This method is used to reshape the output of the convolutional layers into a form that can be passed to the fully connected layer. In this example, we flatten the output of the second convolutional layer into a 1D tensor with size `32 * 8 * 8`.

7. `nn.functional`: This module provides a set of functions that can be used instead of the corresponding layer classes. In this example, we use the `nn.functional.relu` function instead of the `nn.ReLU` layer class.

Write a 15 line readme with nice markdown formatting for a library that helps with medical imaging

# Medical Imaging Library

The Medical Imaging Library is a Python package that provides various tools and functions to help with medical imaging. This library is designed to be user-friendly and easy to use, with a focus on providing high-quality results.

## Features

The Medical Imaging Library includes the following features:

* Image processing functions, such as filtering and segmentation
* Visualization tools for 2D and 3D images
* Support for various image formats, including DICOM and NIfTI
* Registration and fusion of images
* Machine learning algorithms for image classification and segmentation

## Installation

To install the Medical Imaging Library, simply run the following command:

```
pip install medical-imaging
```

## Usage

To use the Medical Imaging Library in your Python project, simply import the package and start using its functions. Here's an example:

```python
import medical_imaging as mi

# Load an image
image = mi.load_image("example.dicom")

# Apply a filter
filtered_image = mi.filter_image(image, "median")

# Segment the image
segmented_image = mi.segment_image(filtered_image)

# Visualize the results
mi.show_image(image)
mi.show_image(filtered_image)
mi.show_image(segmented_image)
```

## Documentation

The full documentation for the Medical Imaging Library can be found at [https://medical-imaging.readthedocs.io/en/latest/](https://medical-imaging.readthedocs.io/en/latest/).

## Contributing

If you would like to contribute to the Medical Imaging Library, please read the [contributing guidelines](CONTRIBUTING.md) before submitting a pull request.

## License

The Medical Imaging Library is licensed under the [MIT License](LICENSE).

## Acknowledgments

This library was inspired by the need for better tools for medical imaging analysis. We would like to thank all the contributors who have helped make this library what it is today.

## Contact

If you have any questions or comments about the Medical Imaging Library, please contact us at [medical-imaging-library@example.com](mailto:medical-imaging-library@example.com).